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Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm.

Nayyer Mostaghim Bakhshayesh1, Mousa Shamsi1, Faegheh Golabi2

  • 1Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.

Journal of Medical Signals and Sensors
|July 12, 2024
PubMed
Summary
This summary is machine-generated.

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This study enhances microarray image analysis by improving contrast and reducing noise in preprocessing. The proposed genetic algorithm method outperforms existing techniques for accurate gene expression data.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray analysis quantifies gene expression using DNA and RNA studies.
  • The process involves sample handling, data extraction via image processing, and data analysis.
  • Microarray image analysis (MAI) requires preprocessing, gridding, segmentation, and intensity quantification.

Purpose of the Study:

  • To improve the preprocessing stage of microarray image analysis.
  • To enhance image contrast and remove noise for better gene expression data.
  • To evaluate the effectiveness of a proposed contrast enhancement (CE) method.

Main Methods:

  • The study focuses on the preprocessing stage of MAI.
  • A genetic algorithm is used for contrast enhancement.
Keywords:
Contrast enhancementgenetic algorithmgenomicsgriddingmathematical morphologymicroarray images

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  • Morphological operations are applied for noise removal.
  • Gridding is performed on complementary deoxyribonucleic acid MAIs to assess CE impact.

    • Main Results:

    • The proposed genetic algorithm-based CE method significantly improves image contrast.
    • The method demonstrates superiority over adaptive histogram equalization (AHE) and multi-decomposition histogram equalization (M-DHE).
    • Example: Contrast increased from 3.24 to 42.91, compared to 13.48 (AHE) and 32.40 (M-DHE).

    Conclusions:

    • The proposed method is effective for enhancing microarray image preprocessing.
    • Performance evaluation across three databases identifies optimal CE methods for specific datasets.
    • The findings contribute to more accurate gene expression identification.